Method and apparatus for estimating an optimal dosage of bleaching agent to be used in a process for producing pulp

ABSTRACT

A method and an apparatus for estimating an optimal dosage of bleaching agent to be used in a process for producing pulp of a required brightness value involve a set of wood chip properties characterizing the wood chips as estimated by a measurement system. Corresponding wood chip properties data are fed at the inputs of a predictive model ( 10 ) including a neural network ( 12 ), as well as an initial dosage value of the bleaching agent. The predictive model ( 10 ) generates a predicted brightness value of pulp to produce from the inspected wood chips, to estimate the optimal bleaching agent dosage for which the predicted brightness value substantially reaches the required brightness value. A method and system for controlling the bleaching of pulp are respectively based on the same estimation method and apparatus.

FIELD OF THE INVENTION

The present invention relates to the field of pulp and paper processautomation, and more particularity to methods for estimating andcontrolling optimal dosage of bleaching agent to be used in a processfor producing pulp of a required brightness value from wood chips.

BACKGROUND OF INVENTION

Thermomechanical pulp properties and quality are influenced by two typesof variables: feed material (chips) and process (refiner). Over theyears, many researchers have underscored the impact of the stability ofthe refiner operation for the production of constant pulp quality, asmentioned by Strand, B. C. in “The Effect of Refiner Variation on PulpQuality”, International Mechanical Pulping Conference, Proceedings,125-130 (1995). However, variations of the process itself are mainlyrelated to variations in the raw material feeding the system as,mentioned by Wood, J. A. in “Chip Quality Effects in MechanicalPulping—a Selected Review”, 1996 TAPPI Pulping Conference, Proceedings,491-497 (1996). In particular, pulp brightness is considered as animportant quality requirement, as discussed by Dence, C. W. et al. in“Pulp Bleaching—Principles and Practice”, TAPPI Press, 457-490 (1996).

SUMMARY OF INVENTION

A main object of the methods, apparatus and system according to theinvention is to estimate the optimal dosage of bleaching agent for thepurpose of control thereof in a pulp production process, by modeling therelationship between the quality of the chips feeding the process withan important pulp and paper resulting property, namely pulp brightness.In particular, the model is used to evaluate the minimum charge ofperoxide required to reach certain level of pulp brightness according topossible chips properties fluctuations, in order to minimize the costand environmental impact of the bleaching operation.

According to the above mentioned object, from a broad aspect of theinvention, there is provided a method for estimating an optimal dosageof bleaching agent to be used in a process for producing pulp of arequired brightness value from wood chips. The method comprises the stepof: i) estimating a set of wood chip properties characterizing said woodchips to generate corresponding wood chip properties data, said setincluding reflectance-related properties; said method beingcharacterized by further comprising the steps of: ii) providing aninitial dosage value of said bleaching agent; and iii) feeding said woodchip properties data and said bleaching agent dosage value atcorresponding inputs of a predictive model for generating predictedbrightness value of pulp to produce from said wood chips, to estimatethe optimal bleaching agent dosage for which said predicted brightnessvalue substantially reaches said required brightness value.

According to the same object, from another aspect of the invention,there is provided a method of controlling the bleaching of pulp in apulp production process on the basis of the optimal bleaching agentdosage estimated according to the above mentioned estimation method,said pulp production process including, between said steps i) and iii),at least one processing step including a step of refining said woodchips to produce refined wood chips. The control method comprises thestep of: a) adding bleaching agent to said refined wood chips accordingto said optimal bleaching agent dosage to produce said pulp.

According to the same object, from another aspect of the invention,there is provided a method of controlling the bleaching of pulp in apulp production process on the basis of the optimal bleaching agentdosage estimated according to the above mentioned estimation method,said pulp production process including, between said steps i) and iii),at least one processing steps including a step of refining said woodchips to produce refined wood chips. The control method comprising thestep of: a) estimating a resulting brightness value of the pulpaccording to a time delay following said predicted brightness valuegeneration; b) comparing said predicted brightness value with saidresulting brightness value to generate further error data; c) furtheroptimizing said bleaching agent dosage value to minimize said furthererror data; and d) adding bleaching agent to said refined wood chipsaccording to said further optimized bleaching agent dosage to producesaid pulp.

According to the same object, from another aspect of the invention thereis provided an apparatus for estimating an optimal dosage of bleachingagent to be used in a process for producing pulp of a requiredbrightness value from wood chips. The apparatus comprises means forestimating a set of wood chip properties characterizing said wood chipsto generate corresponding wood chip properties data, said set includingreflectance-related properties. The apparatus is characterized byfurther comprising: data processor means implementing a predictive modelreceiving at corresponding inputs thereof said wood chip properties dataand an initial bleaching agent dosage value for generating predictedbrightness value of pulp to produce from said wood chips, to estimatethe optimal bleaching agent dosage for which said predicted brightnessvalue substantially reaches said required brightness value.

According to the same object, from another aspect of the invention thereis provided a system of controlling the bleaching of pulp in a pulpproduction process on the basis of the optimal bleaching agent dosageestimated by the above mentioned apparatus, said pulp production processincluding at least one processing steps including a step of refiningsaid wood chips to produce refined wood chips. The control systemcomprises means for adding bleaching agent to said refined wood chipsaccording to said optimal bleaching agent dosage to produce said pulp.

According to the same object, from another aspect of the invention thereis provided a system for controlling the bleaching of pulp in a pulpproduction process on the basis of the optimal bleaching agent dosageestimated by the above mentioned apparatus, said pulp production processincluding at least one processing steps including a step of refiningsaid wood chips to produce refined wood chips. The control systemcomprises means for estimating a resulting brightness value of the pulpaccording to a time delay following said predicted brightness valuegeneration by said predictive model; means for time delaying saidpredicted brightness value according to said time delay; means forcomparing said delayed predicted brightness value with said resultingbrightness value to generate further error data; said predictive modelfurther optimizing said bleaching agent dosage value to minimize saidfurther error data; and means for adding bleaching agent to said refinedwood chips according to said further optimized bleaching agent dosage toproduce said pulp.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, apparatus and system according to the present inventionwill be described in detail with reference to the accompanying drawingsin which:

FIG. 1 is a graph showing relative importance index of independentvariables according to PLS analysis;

FIG. 2 is a graph showing coefficient of correlation for dependentvariables by PLS analysis;

FIG. 3 is a graph representing observed and predicted values for ISObrightness; and

FIG. 4 is a block diagram of a bleaching agent control system accordingto a first embodiment of the invention, which includes an estimationapparatus based on a neural network-based predictive model;

FIG. 5 is a block diagram of a bleaching agent control system accordingto another embodiment of the invention, which is particularly adaptedfor controlling a bleaching operation as part of a continuous pulpproduction process.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The methods for estimating an optimal dosage of bleaching agent of thepresent invention being based on the estimation of properties of woodchips that must have significant effect on the bleaching characteristicsof the pulp made therefrom, an experimental protocol used to qualifywood chip properties to be preferably used in modeling will be presentedfirst. In order to define the parameters used for the model, two sets ofexperiments corresponding to two different blocks were performed. In thefirst block, a potential mix of four species, black spruce, balsam fir,jack pine and white birch, was studied. The last two species were chosenbecause they represent a potential source of new resources. The treeshave been selected, cut, barked and chipped in order to obtain standardchips with known and controlled age. In fall, outdoor stacks of eachspecies of chips were prepared. During the following 12 months, sixsamples were selected in order to conduct the experimental plan forchips aging as described in table 1. TABLE 1 Test number Spruce % Balsamfir % Jack pine % Birch % 1 0 0.2 0.4 0.4 2 1 0 0 0 3 0 1 0 0 4 0.6 0 00.4 5 0 0.6 0.4 0 6 0.6 0 0.4 0 7 0 0.6 0 0.4 8 0.2 0 0.4 0.4 Followingare repetitions for experimental error determination 9 1 0 0 0 10 0 1 00 Following are additional tests 11 0 0 1 0 12 0 0 0 1In each sample, the experiments for 100% black spruce and 100% balsamfir were repeated twice in order to evaluate the experimental error andtwo additional tests for 100% jack pine and 100% birch (12 runs in eachsample). The six samples allow to evaluate the evolution of the quality,i.e. degradation, of the chips in time. This degradation is highlydependent on storage temperature. The first four samples were evaluatedat an interval of three weeks. After that, there has been a longerwaiting time. It was noticed that the winter degradation of each stackwas extremely slow.

The second block of experiments was used to investigate the effects ofother important variables regarding pulp quality. This second block ofexperiments has been conducted with four variables: species (blackspruce, balsam fir), density (high, low), initial dryness of the chips(fresh, dry), and thickness of the chips (0-4 mm, 4-8 mm). Table 2describes the experiments for chips aging that were conducted in thissecond block. TABLE 2 Large chips Small chips (4-8 mm) 0-4 mm INITIALCHIPS Spruce at low density Test no. 1 Test no. 2 Balsam fir at lowdensity Test no. 3 Test no. 4 Spruce at high density Tests no. 5 and 6Test no. 7 Balsam fir at high density Test no. 8 Tests no. 9 and 10 Agedchips (dryness of 75%) Spruce at low density Test no. 11 Tests no. 12and 13 Balsam fir at low density Tests no. 14 and 15 Test no. 16 Spruceat high density Test no. 17 Test no. 18 Balsam fir at high density Testno. 19 Test no. 20For the purpose of the experiment, the estimation and control methodaccording to the invention was applied to a batch pulp productionprocess. Refining was conducted on a pilot unit Metso CD-300. Eachsample was washed and refined in two stages. The first one was conductedat a temperature of 128° C. and the second one at atmospheric pressure.For each experiment, pulps with a freeness ranging from 200 to 150 mLwere selected for further peroxide bleaching, which fundamentalprinciples are briefly described next.

It is generally accepted that the active mechanism in chromophoreelimination with hydrogen peroxide as bleaching agent involves theperhydroxyl ion OOH⁻. As taught by Sundholm, J. in “Papermaking Scienceand Technology—Mechanical Pulping”, Finnish Pulp and Paper ResearchInstitute, 313-345 (1999), hydrogen peroxide bleaching is thereforeperformed in alkaline systems to produce the active ion according to thefollowing equation:H₂O₂+OH⁻→OOH⁻+H₂O  (1)The formation of the perhydroxyl anion can be enhanced by increasing thepH or by increasing the temperature. Hydrogen peroxide readilydecomposes under bleaching conditions according to the followingequation:2H₂O₂→O₂+H₂O  (2)Sodium silicate and magnesium silicate are normally added to the bleachliquor to stabilize peroxide. Transition metals ions like iron,manganese and copper catalyze peroxide decomposition. In order topreventThat, before bleaching with peroxide, the pulp was pretreated with 0.2%of DTPA. The pretreatment of the pulp was done at 60° C., 15 minutes and3% of consistency.

Different concentrations of hydrogen peroxide varying from 1 to 5% (O.D.basis) were tested for bleaching the different pulp. Table 3 describesthe experimental conditions used for the peroxide bleaching of thepre-treated pulps. TABLE 3 Parameters P1 P2 P3 P5 Temperature, ° C. 7070 70 70 Retention time, min 180 180 180 180 Consistency, % 12 12 12 12Sodium silicate, % 3.00 3.00 3.00 3.00 Magnesium sulfate, % 0.05 0.050.05 0.05 Total Alcali Ratio 2.00 1.20 0.90 0.80 Sodium hydroxyde, %1.66 2.06 2.36 3.66 Hydrogen peroxide, % 1.00 2.00 3.00 5.00where: $\begin{matrix}{{{Total}\quad{Alcali}\quad{Ratio}} = \frac{\%\quad{Hydrogen}\quad{Peroxide}}{{{\%\quad{OH}}\quad - {{given}\quad{by}\quad{sodium}\quad{silicate}\quad{and}\quad{sodium}\quad{hydroxide}}}\quad}} & (3)\end{matrix}$Bleaching was conducted at 70° C., 180 minutes and 12% of consistency.The bleaching liquor was composed of 3.00% of sodium silicate, 0.05% ofmagnesium sulfate, hydrogen peroxide and sodium hydroxide. After thebleaching step, the pulp was diluted at 1% of consistency andneutralized with sodium metabisulfite at pH 5.5. A volume of thebleaching liquor was kept to measure the residual peroxide by aniodometric dosage. Optical properties such as ISO brightness and colorcoordinates (L*, a*, b*) have been measured according to Paptacstandard.

Chips of the eighty four (84) runs in block 1 and twenty (20) runs inblock 2 were systematically analyzed using a wood chip opticalinspection apparatus known as CMS-100 chip management systemcommercially available from the present assignee, Centre de RechercheIndustrielle du Quebec (Ste-foy, Canada), for measuring a number ofoptical properties as well as moisture content. Such wood chipinspection apparatus is described in U.S. Pat. No. 6,175,092 B1 issuedon Jan. 16, 2001 to the present assignee. Such multi-sensor systemincludes main and optional auxiliary sensors able to characterize woodchips online. The main sensors include artificial vision sensor (an RGBcolor camera) and near infrared sensor to measure chip brightness andmoisture content. Auxiliary sensors such as a distance sensor and an airconditions sensor to measure air temperature and relative humidity maybe advantageously used. They provide information that extendsmeasurements of the main sensor to stabilize the system (for example,variations of the camera measuring distance will influence the chipbrightness measurement). The system will work on frozen and non-frozenwood chips, and it used for predicting bleach charges or dosage based onchip quality for use as a bleach control method or system. Thecorrelation between some chip properties and its possible application inbleach control is discussed by Ding, F. et al. in “Economizing theBleaching Agent Consumption by Controlling Wood Chip Brightness”,Control System 2002, Proceedings, June 3-5, Stockholm, Sweden, 205-209(2002). The most relevant wood chips properties measurements for thepurpose of the present invention are described next.

A first measurement relates to chip luminance, wherein the brightness ofblack is defined as zero and the brightness of white as 150. The RGBcolour camera of the system is calibrated by a color checker made ofblack and white paperboard. The wood chip color is between white andblack, so its brightness is between 0 and 150. A second measurementrelates to chip average moisture content. The system includes a nearinfrared sensor such as model NDC 55 supplied by Korins Co. Ltd.(Korea), that is used to measure surface moisture content of wood chips,without any non-contact therewith. A method for estimating surfacemoisture of wood chips that can be used for the purpose of theestimation method of the present invention is disclosed by Ding, F. etal. in “Wood Chip Physical Quality Definition and Measurement”, IMPCProceedings, June 2-5, Québec, Canada, 367-373 (2003). Aphenomenological model may also be used to calculate the averagemoisture content from surface moisture content, as described by Ding, F.et al. in “Economizing the Bleaching Agent Consumption by ControllingWood Chip Brightness”, Control System 2002, Proceedings, June 3-5,Stockholm, Sweden, 205-209 (2002). Other measurements may be obtainedfrom various further sensors, generating a large amount of datacategorized in many different variables. According to the presentpreferred embodiment, a number of four (4) other measurements areconsidered, namely the image “H”, “S” and “L” parameters, as well as achip average size estimation, which may be obtained using animaging-based, chip size classifier such as the ScanChip™ systemsupplied by Iggesund Tools Inc. (Oldsmar, Fla., USA). Alternatively, asampling-based size estimation method according to a known standard suchas William size classifying protocol may be used to provide chip sizedata. Other color imaging standard measurements such as “R G B” or “LAB”may be also used to characterize reflectance-related characteristics ofwood chips.

The database resulting from the various experiments gives rise to three(3) types of variables: chip properties coming from the measuringsystem, operational parameters of the TMP and bleach processes, and pulpquality characteristics. Overall, the database used contained a largenumber (n=178) of variables distributed over a corresponding number ofcolumns. Because all (104) runs for both blocks produced pulps whichwere bleached at four (4) different peroxide charges, the database alsocontains four times (416) runs distributed over a corresponding numberof lines. In order to capture possible system measurements errors, thedatabase contained many repeated measurements for the same chips,leading a final database containing a still greater number (506) of datalines. In the following sections, the techniques that are preferablyused to screen the columns of data to a reasonable amount of mostrelevant variables and to use the lines for neural network training willbe explained. Both techniques are done with the objective of obtaining agood enough pulp brightness model that could be used in a brightnesscontrol strategy.

The data screening to perform the selection of the independent variableswhich have an effect on the dependent variables that have been measuredis preferably done using known PLS (Projection on a Latent Structure)modeling. FIG. 1 presents the independent variables that have beenchosen according their relative importance index. As expected, theparameters, which have most impact and are correlated to dependentvariables, are the concentration of sodium hydroxide (NaOH) and theconcentration of hydrogen peroxide (PEROA). After that, the variablesCO_moy (chip size), MDH (average of H), MMLC (average of luminance), MDS(average of S), MDL (average of L) and MSURFM (average of the surfacemoisture) also contribute to a lesser extent to the bleached pulpproperties response.

The correlation coefficients for each dependent variable are presentedin FIG. 2. The value R2 shows the correlation for the dependentvariables. It is an indication of how well the model can fit theexperimental data. The value Q2 shows the correlation of theinterpolated responses, i.e. predictions not part of the experimentaldata. The graph shows that the model is adequate to predict ISObrightness [ISOB] (coefficient of correlation of 0.88), colorcoordinates L*[LB] (coefficient of correlation of 0.92) and a*[AB](coefficient of correlation of 0.90), and residual peroxide [PEROR](coefficient of correlation of 0.83). As for the color coordinateb*[BB], the coefficient of correlation is only 0.60. We also note thatchemical properties such as MEXT (extractives) and AGR (fatty andresinic acids) are difficult to correlate (coefficients of correlationof 0.33 and 0.26 are respectively obtained).

FIG. 3 presents observed and predicted values for the ISO brightness.These results show that the model is able to predict adequate values forthis optical property. Brightness ranging from 43.79% to 80.2% wasmeasured on the bleached pulps.

A neural network-based predictive model that can be used to carry outthe method according to the invention will now be described in referenceto FIG. 4. It is to be understood that any appropriate modelingtechnique such as neural network, PLS, Model Predictive Controller(MPC), regression, state space matrix, FRI, fuzzy logic, geneticalgorithm, or a combination thereof can be used to obtain a predictivemodel for the purpose of the present invention. Some of those knownpredictive modeling techniques are discussed by Quian, X. et al. in“Mechanistic Model for Predicting Pulp Properties from Refiner OperatingConditions” TAPPI Journal, 78 (4) (1994); by Qian, Y. et al. in “FuzzyLogic Modeling and Optimization of a Wood Chip Refiner” TAPPI Journal,77 (2) (1995), and by Qian, Y. et al. in “Modeling a Wood-Chip RefinerUsing Artificial Neural Networks, TAPPI Journal, 78 (6): 167-174 (1995).The predictive model generally designated at 10 and as readilyimplemented in a data processing device such as a computer (not shown)provided on the bleaching agent dosage estimating apparatus andbleaching control system represented in FIG. 4, preferably includes aneural network 12 that was previously trained according go to theexperimentally obtained data on wood chip properties and on dosage ofsaid bleaching agent as described above, i.e. over the nine (9)remaining database columns consisting of eight (8) inputs identified byPLS method as shown in FIG. 1, and one output, namely pulp brightness asshown in FIG. 3. Such known neural network and associated trainingapproach are discussed by Laperriere L. et al. in Modeling andsimulation of pulp and paper quality. After a few unsuccessful trainingtrials, it was noticed that the input NaOH is always a ratio of theinput H2O2, so it was eliminated from the training set. Out of theavailable (506) training lines, a selected number (96) were removed(about 20%) and injected back to the trained network for validation.Different sets of the removed 20% were tested and gave similar results.The final configuration was a 7-5-1 neural network (7 inputs, 5 hiddenneurons and 1 output) as designated at 12 in FIG. 4. Training wasstopped after an average absolute mean error of 5% was reached betweenthe neural network prediction and the training output brightness valuefor each of the 506 lines. The value of 5% was chosen by taking twofactors into consideration: 1) reliability of the output measurements:the experimental error related to the brightness value is about 3%, i.e.±0.5 brightness points in the experimental span of 43.79 to 80.2measured brightness; and 2) reliability of the input measurements:calibration errors may encourage an increase of the training error. Thetraining results of the final network, in terms of the connectionweights between each of its constituting neurons, were imported into theneural network 12 of model 10, in the form of a computer program thatcan be implemented in a microcomputer by any person skilled in the artusing well-known programming tools. Such program is able to simulatebrightness prediction based on the seven (7) chosen inputs, namelyreflectance-related properties of wood chips that are Luminance, M, H,S, L and chip size from measurement system 14 as part of the bleachingagent dosage estimating apparatus, and bleaching agent dosage (peroxidecharge) value used by the bleaching unit 16 as part of the bleachingcontrol system, to add a corresponding volume of bleaching agentsolution into the pulp made of refined wood chips to produce bleachedpulp. Optionally, unmodeled disturbances may also be applied to theneural network at input 17

In operation, according to the set of wood chip propertiescharacterizing the wood chips as estimated by the measurement system,corresponding wood chip properties data are fed at respective inputs 18of neural network 12, as well as an initial dosage value of thebleaching agent (peroxide) at further input 20. Although input 20 ispreferably used to receive bleaching agent dosage as actually fed topulp as typically calculated from a flow meter measurement at bleachingunit outlet, knowing agent concentration and pulp weight, the initialdosage may be set to a predetermined value for the purpose ofinitializing the prediction model. In turn, the neural network 12generates at output 22 thereof, a predicted brightness value for pulp toproduce from the inspected wood chips. Then, the brightness predictedvalue is compared with the required brightness value to generate errordata, as indicated at node 24. In turn, the error data is used by anoptimization module 26, which optimizes the bleaching agent dosage valueto minimize the error data. Finally, the above prediction, comparisonand optimization steps are repeated with the optimized bleaching agentdosage value as fed back to the network 12 at input 25 thereof, untilthe brightness predicted value substantially reaches the requiredbrightness value, to estimate the optimal bleaching agent dosage. Inother words, the peroxide charge is tuned to minimize the error, whilemaintaining constant chip properties, and an optimization loops isperformed in model 10 for several iterations before it reaches theperoxide charge that meets the required brightness value or set pointaccording to the neural network model prediction. When this optimalvalue has been found, it can be sent back to the actual process throughcontrol switch 27 and control input 28 of bleaching unit 26 forcorrective action on a control valve (not shown) provided on bleachingunit 26. Such control strategy assumes that the time taken for theoptimization to take place is less than the frequency at whichbrightness set points will be modified, which is a reasonableassumption.

Because the brightness prediction is based upon variables that arealgorithmic transformations of camera signals, a first simulation wasdesigned in which the neural network model was used in conjunction withan optimizer that would find the best combination of measurement systeminput variables that would give the best achievable brightness.Simulation results are shown in table 4. TABLE 4 Variable Min.experimental value Max. experimental value Luminance 13.23 57.93Moisture 19.54 70.34 Size 16.49 21.39 H 19.29 224.44 S 82.08 192.89 L9.17 66.85 H2O2 0.00 5.00There are two main observations from this result. First, for optimalbrightness all independent variables are either at the minimum ormaximum values or their respective span. This means that the hypersurface for which a minimum was found slants towards an intersection ofthe constraint hyper planes corresponding to the maximum or minimumvalues of each independent variable. This also means there is awell-defined combination for maximum brightness (the optimal combinationwas consistently reproduced for many different simulation trials).Second, we see that five (5) of the six (6) system measurements give thebest pulp brightness when they are at their higher values, except forthe “H” parameter (lowest value).

Turning back to FIG. 1, the sensitivity of output properties withrespect to some of the chosen inputs is shown. Because this result wasobtained from a PLS model and the use a neural network model iscontemplated, another set of simulations was run to verify if thesensitivity of the sole pulp brightness to each independent variablewould be similar. Table 5 shows a series of tests where each variablewas given sinusoidal swings of its value over its total experimentalspan as shown in table 4, while maintaining other variables at theircentral values, to show brightness sensitivity to the independentvariables. TABLE 5 Variable Min. brightness Max. brightness % change inbrightness Luminance 63.55 68.02 12.3 Moisture 64.52 67.01 6.8 Size64.60 67.48 7.9 H 63.81 67.71 10.7 S 63.99 67.69 10.1 L 65.18 66.52 3.6H2O2 49.57 70.83 58.4It turns out that peroxide has a predominant effect. In fact, theperoxide charge fixes the brightness level and changes in the chipproperties simply add small variations around the level attained. Everysystem measurement variable, when bumped independently within its fullspan, contributes to small percentage of change around the brightnesslevel dictated by the peroxide charge.

In order to illustrate brightness control feature, a first set ofsimulation results is shown in table 6, representing the effect of chipquality on peroxide charges to achieve different brightness set points.TABLE 6 Peroxyde Peroxyde charge (%) Brightness charge (%) best setpoint average quality theoretical Peroxyde charge (%) best (%) chipschips experimental chips 55 0.77 0.0 0.15 60 1.41 0.0 0.76 65 2.22 0.351.48 70 4.12 1.21 2.92 71 4.99 1.48 3.54 75 Unachievable 4.96Unachievable (max 71%) (max 72%)All measurement system parameters (chip properties) were maintained attheir average value and brightness set point was bumped from 55 to 75 byincrements of (five) 5 points. For these “average” chips, one can seethat a 38% increase in peroxide (from 2.22 to 4.12%) is required toincrease the brightness level from 65 to 70 points (13.7%), and that afurther 17.6% increase (from 4.12 to 5%) is required to gain only 1brightness point (from 70 to the maximum achievable 71) Doing the samething with the theoretical best possible chips as per table 4, one cannote that no peroxide is required until a brightness set point close to65 is desired. Also, a 71 brightness is achievable with only 1.48%peroxide. Finally, further gains in brightness points from 71 to 75 areonly obtained at a high peroxide cost (from 1.48 to 4.96%). Because onecannot assume that chips with such properties actually exist, the chipproperties contained in the above-mentioned database were also used,which returned the best brightness value at 72.46. In this case, thebetter chip properties still reduce peroxide consumption for the samebrightness level, but to a lesser extent.

A bleaching agent control system according to another embodiment of theinvention, which is particularly adapted for controlling a bleachingoperation as part of a continuous pulp production process will now bedescribed with reference to FIG. 5. Such continuous pulp productionprocess includes, between raw wood chips supplying step, where the woodchips properties are measured, and bleaching step at least one stepconsisting of refining chips, and generally a plurality of otherprocessing or handling steps each being characterized by a specificprocessing time, involving equipment such as chests and towers forperforming process functions such as storage, mixing and transfer onvarious pulp matter such as accepted pulp, unrefined reject, refinedreject, screened reject, etc. In order to adequately estimating andcontrolling the bleaching dosage for such continuous pulp productionprocess, the estimated wood chips properties may be used by thepredictive model according to the invention only if the time delaybetween wood properties estimation and bleaching steps as induced by theintermediate processing steps is considered by the bleaching agentestimation method. For so doing, the bleaching agent estimationapparatus and bleaching control system shown in FIG. 5 is provided witha time delay module 30 receiving all wood chip properties data, namelyluminance, moisture content”, H, S, L and size data, to apply thereto atime delay value, which can be either a fixed value in the case of asimple process involving few intermediate processing steps, or acalculated value in the case of a more complex process, usinginput/output process parameters including pulp consistency, pulpweight/mass flow rate, chest/tower volume and filling levels, etc. Forso doing, basic dynamic calculation or a more advanced modelingtechnique such as neural networks, fuzzy logic, genetic algorithms, or acombination thereof along with active mass balance data may be used byany person skilled in the computer programming for the purpose ofimplementing the desired time delay. Moreover, the processingchest/towers used in the process between chip properties estimation andbleaching operation induce an attenuation of the actual wood chipproperties, and therefore, the estimated wood chip properties data mustbe filtered accordingly, preferably using an attenuation filter 32receiving the delayed chip properties data from time delay 30, andfeeding the resulting chip property data to inputs 18 of the predictivemodel 10. Here again, basic dynamic calculation or a more advancedmodeling technique may be used by any person skilled in the computerprogramming for the purpose of implementing the desired attenuationfiltering. In a case where the bleaching unit discharge is located onthe main pulp line containing accepted pulp and treated reject pulp,some difference in properties will be observed between accepted pulp andtreated reject pulp, which difference will be influenced by the rejectedpulp treating rate as well as the delay induced by each chest involved.Such pulp properties difference may be also compensated in a similarmanner as described above, using either dynamic calculation or anadvanced modeling technique.

Referring again to FIG. 5, it can be seen that the model 10, in additionto receiving the required brightness (set point) at input 34 thereof ina same manner as explained above with reference to the embodiment shownin FIG. 4, receives further error data at input 40, in order to furtherimprove bleaching dosage estimation, as will now be explained in detail.Since the optimal dosage estimation is based on a prediction by themodel 10 of the resulting, final brightness of the bleached pulp, anestimation of the resulting, actual pulp brightness as obtained eitherwith an online measurement sensor (not shown) or following an off-lineanalyzing procedure on a pulp sample (Paptac standard), is madeaccording to a time delay following the predicted brightness valuegeneration by the predictive model 10. Conveniently, such measurementmay be made at the outlet of bleaching chest/tower, or be made at thescanning station of the paper machine by implanting an appropriate modelconsidering active mass balance data and respective chip properties dataassociated with the various pulp matters used to produce the paper. Atime delay 36 is provided for delaying the predicted brightness valueaccording to the time delay, which may be either a fixed value or acalculated value obtained through dynamic calculation or advancedmodeling in a similar manner as explained above. A comparator 38 isprovided for comparing the delayed predicted brightness value with theresulting brightness value to generate further error data that is fedback to input 40 of predictive model 10, which can further optimize thebleaching agent dosage value to minimize further error data. Uponreceiving further optimized bleaching dosage data generated by the model10, the bleaching unit 28 is caused to discharge a corresponding amountof bleaching agent solution, and the applied dosage measurement is fedback to the input 20 of model 10 in a same manner as explained beforewith respect to the embodiment shown in FIG. 4. The bleaching agentaddition control function may be conveniently performed through a modelimplemented in the data processing device, generating one or more massflow set points for the bleaching agent so as to better regulate theprocess.

When using the methods, apparatus and system according to the invention,the same brightness set point can be achieved at lower bleaching agentcharges when the chip quality increases. The method may be useful toassist chip management in the mill, or in the context of internal modelcontrol (IMC) or model predictive control (MPC) strategies. It is to beunderstood that dosage of other bleaching agents such as hydrosulfitesmay also be performed with the method of the invention.

We claim:
 1. A method for estimating an optimal dosage of bleachingagent to be used in a process for producing pulp of a requiredbrightness value from wood chips, said method comprising the step of: i)estimating a set of wood chip properties characterizing said wood chipsto generate corresponding wood chip properties data, said set includingreflectance-related properties; said method being characterized byfurther comprising the steps of: ii) providing an initial dosage valueof said bleaching agent; and iii) feeding said wood chip properties dataand said bleaching agent dosage value at corresponding inputs of apredictive model (10) for generating predicted brightness value of pulpto produce from said wood chips, to estimate the optimal bleaching agentdosage for which said predicted brightness value substantially reachessaid required brightness value.
 2. The method according to claim 1,wherein said set of wood chips properties further includes wood chipsize.
 3. The method according to claim 1, wherein said set of wood chipsproperties further includes moisture.
 4. The method according to claim1, wherein said predictive model estimate the optimal bleaching agentdosage by performing the steps of: a) comparing said brightnesspredicted value with said required brightness value to generate errordata; b) optimizing said bleaching agent dosage value to minimize saiderror data; and c) repeatedly generating predicted brightness value andperforming said steps a) and b) with the optimized bleaching agentdosage value until said predicted brightness value substantially reachessaid required brightness value, to estimate said optimal bleaching agentdosage.
 5. The method according to claim 1, wherein said predictivemodel includes a neural network (12) previously trained according toexperimentally obtained data on said wood chip properties and on dosageof said bleaching agent.
 6. A method of controlling the bleaching ofpulp in a pulp production process on the basis of the optimal bleachingagent dosage estimated according to the method of claim 1, said pulpproduction process including, between said steps i) and iii), at leastone processing step including a step of refining said wood chips toproduce refined wood chips, said control method comprising the step of:a) adding bleaching agent to said refined wood chips according to saidoptimal bleaching agent dosage to produce said pulp.
 7. The methodaccording to claim 6, wherein said pulp production process iscontinuous, said set of wood chip properties data are filtered accordingto an attenuation of said estimated wood chip properties caused by saidat least one processing step.
 8. The method of claim 6, furthercomprising the step of: b) repeating said step i) to generate wood chipproperties data corresponding to a new estimation of the set of woodchip properties characterizing said wood chips; c) repeating said stepiii) with said optimal bleaching dosage to provide a new estimation ofthe optimal bleaching dosage; and d) adding bleaching agent to saidrefined wood chips according to said new estimation of bleaching agentdosage to produce said pulp.
 9. A method of controlling the bleaching ofpulp in a pulp production process on the basis of the optimal bleachingagent dosage estimated according to the method of claim 1, said pulpproduction process including, between said steps i) and iii), at leastone processing steps including a step of refining said wood chips toproduce refined wood chips, said control method comprising the step of:a) estimating a resulting brightness value of the pulp according to atime delay following said predicted brightness value generation; b)comparing said predicted brightness value with said resulting brightnessvalue to generate further error data; c) further optimizing saidbleaching agent dosage value to minimize said further error data; and d)adding bleaching agent to said refined wood chips according to saidfurther optimized bleaching agent dosage to produce said pulp.
 10. Anapparatus for estimating an optimal dosage of bleaching agent to be usedin a process for producing pulp of a required brightness value from woodchips, said apparatus comprising: means (14) for estimating a set ofwood chip properties characterizing said wood chips to generatecorresponding wood chip properties data, said set includingreflectance-related properties; said apparatus being characterized byfurther comprising: data processor means implementing a predictive model(10) receiving at corresponding inputs thereof said wood chip propertiesdata and an initial bleaching agent dosage value for generatingpredicted brightness value of pulp to produce from said wood chips, toestimate the optimal bleaching agent dosage for which said predictedbrightness value substantially reaches said required brightness value.11. The apparatus according to claim 10, wherein said predictive model(10) includes: a) means (24) for comparing said brightness predictedvalue with said required brightness value to generate error data; and b)means (26) for optimizing said bleaching agent dosage value to minimizesaid error data.
 12. The apparatus according to claim 10, wherein saidpredictive model (10) includes a neural network previously trainedaccording to experimentally obtained data on said wood chip propertiesand on dosage of said bleaching agent.
 13. A system of controlling thebleaching of pulp in a pulp production process on the basis of theoptimal bleaching agent dosage estimated by the apparatus according toclaim 10, said pulp production process including at least one processingsteps including a step of refining said wood chips to produce refinedwood chips, said control system comprising means (16) for addingbleaching agent to said refined wood chips according to said optimalbleaching agent dosage to produce said pulp.
 14. A system forcontrolling the bleaching of pulp in a pulp production process on thebasis of the optimal bleaching agent dosage estimated by the apparatusaccording to claim 10, said pulp production process including at leastone processing steps including a step of refining said wood chips toproduce refined wood chips, said control system comprising: means forestimating a resulting brightness value of the pulp according to a timedelay following said predicted brightness value generation by saidpredictive model (10); means for time delaying said predicted brightnessvalue according to said time delay; means (38) for comparing saiddelayed predicted brightness value with said resulting brightness valueto generate further error data; said predictive model (10) furtheroptimizing said bleaching agent dosage value to minimize said furthererror data; and means (16) for adding bleaching agent to said refinedwood chips according to said further optimized bleaching agent dosage toproduce said pulp.
 15. The system according to claim 14, wherein saidpulp production process is continuous, said system further comprisingmeans for filtering said set of wood chip properties data according toan attenuation of said estimated wood chip properties caused by said atleast one processing step.